Gender: Male 
Age: 25-34
Nationality: United Kingdom
Ethnicity: White / Caucasian 
Marital status: Married 
First language: English 
Religion: Christian 
Education: Graduate degree
Mother's education: Graduate degree
Father's education: Doctorate or above
Self-assessed socioeconomic status (1-10): 9
Home: Own 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer);Clerical support workers (those who perform clerical duties such as organizing, storing, computing and retrieving information)
Mother's occupation: Manager or business owner (those who plan, direct, coordinate and evaluate the overall activities of enterprises, governments and other organizations)
Father's occupation: Clerical support workers (those who perform clerical duties such as organizing, storing, computing and retrieving information)
Hobbies: Use social media;Go to the opera;Go to gigs;Watch sports;Exercise 
Devices used daily: Laptop;Tablet;Smartwatch;Smartphone 
NLP tools known: Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools they would find useful: Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa) 
Frequency of LLM use: Every day
LLMs used: ChatGPT;Claude;GitHub Copilot;Pi;Snapchat My AI;Google Gemini;Character.AI;Microsoft Bing AI 
LLM use cases: Translation;Digital content creation (e.g., creation of social media content);Learning (e.g., explaining complex concepts in simple terms);Answering questions about general knowledge;Summarizing long texts;Analyze data;Paraphrasing or finding synonyms;Writing (e.g., composing emails);Brainstorming;Coding;Generate art;Completing assignments;Get information about current events;Collecting references;Proofreading/Editing (e.g., grammar, spelling, structure improvements) 
Contexts of LLM use: Work;School/University;Learning;Entertainment;Creative or artistic;Technical;Personal 
Prompt 1: Words that start with ba and ends with le
Prompt 2: Organization in the uk
Prompt 3: Creative ideas to help theatre
Prompt 4: Come up with a creative idea for a new kind of dining table that involves new features and new concept that does not exist in the market in simple terms
Prompt 5: A verse about giving back what you lost
Prompt 6: 9. Around 40% of pupils do not achieve a grade 4+ in GCSE Maths in year 11. Around half of these go on to gain a grade 4+ in GCSE Maths by age 19. Why do you think 20% reach this standard in 16-19 education but not in secondary education? *
Prompt 7: By age 19, just under 20% of students have not achieved a grade 4+ in GCSE Maths. Why do you think this is? * 11. How do you think we should best support students who do not achieve a grade 4+ at GCSE Maths in year 11? *
Prompt 8: Throw back la n je ni 2025 translate
Prompt 9: Give some examples on the act of giving 100 words
Prompt 10: How to encourage someone who’s faith is failing with a bible qoute in 100 words
Comments: IT WAS AN INTERESTING SURVEY I LOVE IT